Abstract: Many studies have applied the Theory of Planned
Behavior (TPB) in predicting health behaviors among unique
populations. However, a new paradigm is emerging where focus is
now directed to modification and expansion of the TPB model rather
than utilization of the traditional theory. This review proposes new
models modified from the Theory of Planned Behavior and suggest
an appropriate study design that can be used to test the models within
physical activity and dietary practice domains among Type 2
diabetics in Kenya. The review was conducted by means of literature
search in the field of nutrition behavior, health psychology and
mixed methods using predetermined key words. The results identify
pre-intention and post intention gaps within the TPB model that need
to be filled. Additional psychosocial factors are proposed to be
included in the TPB model to generate new models and the efficacy
of these models tested using mixed methods design.
Abstract: Composite laminates are relatively weak in out of
plane loading, inter-laminar stress, stress concentration near the edge
and stress singularities. This paper develops a new analytical
formulation for laminated composite rotating disc fabricated from
symmetric sequential quasi isotropic layers to predict three
dimensional stress and deformation. This analysis is necessary to
evaluate mechanical integrity of fiber reinforced multi-layer
laminates used for high speed rotating applications such as high
speed impellers. Three dimensional governing equations are written
for rotating composite disc. Explicit solution is obtained with
"Frobenius" expansion series. Based on analytical results, there are
two separate zones of three dimensional stress fields in centre and
edge of rotating disc. For thin discs, out of plane deformations and
stresses are small in comparison with plane ones. For relatively thick
discs deformation and stress fields are three dimensional.
Abstract: The prediction of transmembrane helical segments
(TMHs) in membrane proteins is an important field in the
bioinformatics research. In this paper, a method based on discrete
wavelet transform (DWT) has been developed to predict the number
and location of TMHs in membrane proteins. PDB coded as 1F88 was
chosen as an example to describe the prediction of the number and
location of TMHs in membrane proteins by using this method. One
group of test data sets that contain total 19 protein sequences was
utilized to access the effect of this method. Compared with the
prediction results of DAS, PRED-TMR2, SOSUI, HMMTOP2.0 and
TMHMM2.0, the obtained results indicate that the presented method
has higher prediction accuracy.
Abstract: Numerical analysis of flow characteristics and
separation efficiency in a high-efficiency cyclone has been performed.
Several models based on the experimental observation for a design
purpose were proposed. However, the model is only estimated the
cyclone's performance under the limited environments; it is difficult to
obtain a general model for all types of cyclones. The purpose of this
study is to find out the flow characteristics and separation efficiency
numerically. The Reynolds stress model (RSM) was employed instead
of a standard k-ε or a k-ω model which was suitable for isotropic
turbulence and it could predict the pressure drop and the Rankine
vortex very well. For small particles, there were three significant
components (entrance of vortex finder, cone, and dust collector) for
the particle separation. In the present work, the particle re-entraining
phenomenon from the dust collector to the cyclone body was observed
after considerable time. This re-entrainment degraded the separation
efficiency and was one of the significant factors for the separation
efficiency of the cyclone.
Abstract: recurrent neural network (RNN) is an efficient tool for
modeling production control process as well as modeling services. In
this paper one RNN was combined with regression model and were
employed in order to be checked whether the obtained data by the
model in comparison with actual data, are valid for variable process
control chart. Therefore, one maintenance process in workshop of
Esfahan Oil Refining Co. (EORC) was taken for illustration of
models. First, the regression was made for predicting the response
time of process based upon determined factors, and then the error
between actual and predicted response time as output and also the
same factors as input were used in RNN. Finally, according to
predicted data from combined model, it is scrutinized for test values
in statistical process control whether forecasting efficiency is
acceptable. Meanwhile, in training process of RNN, design of
experiments was set so as to optimize the RNN.
Abstract: The objective of this contribution is to study the
performances in terms of bit error rate, of space-time code algorithms
applied to MIMO communication in tunnels. Indeed, the channel
characteristics in a tunnel are quite different than those of urban or
indoor environment, due to the guiding effect of the tunnel.
Therefore, MIMO channel matrices have been measured in a straight
tunnel, in a frequency band around 3GHz. Correlation between array
elements and properties of the MIMO matrices are first studied as a
function of the distance between the transmitter and the receiver.
Then, owing to a software tool simulating the link, predicted values
of bit error rate are given for VLAST, OSTBC and QSTBC
algorithms applied to a MIMO configuration with 2 or 4 array
elements. Results are interpreted from the analysis of the channel
properties.
Abstract: This paper presents a computational study of the separated flow in a planer asymmetric diffuser. The steady RANS equations for turbulent incompressible fluid flow and six turbulence closures are used in the present study. The commercial software code, FLUENT 6.3.26, was used for solving the set of governing equations using various turbulence models. Five of the used turbulence models are available directly in the code while the v2-f turbulence model was implemented via User Defined Scalars (UDS) and User Defined Functions (UDF). A series of computational analysis is performed to assess the performance of turbulence models at different grid density. The results show that the standard k-ω, SST k-ω and v2-f models clearly performed better than other models when an adverse pressure gradient was present. The RSM model shows an acceptable agreement with the velocity and turbulent kinetic energy profiles but it failed to predict the location of separation and attachment points. The standard k-ε and the low-Re k- ε delivered very poor results.
Abstract: Researchers have long had trouble in measurement of
Exchangeable Sodium Ratio (ESR) at salt-affected soils. this
parameter are often determined using laborious and time consuming
laboratory tests, but it may be more appropriate and economical to
develop a method which uses a more simple soil salinity index. The
aim of this study was to determine the relationship between
exchangeable sodium ratio (ESR) and sodium adsorption ratio (SAR)
in some salt-affected soils of Khuzestan plain. To this purpose, two
experimental areas (S1, S2) of Khuzestan province-IRAN were
selected and four treatments with three replications by series of
double rings were applied. The treatments were included 25cm,
50cm, 75cm and 100cm water application. The statistical results of
the study indicated that in order to predict soil ESR based on soil
SAR the linear regression model ESR=0.2048+0.0066 SAR
(R2=0.53) & ESR=0.0564+0.0171 SAR (R2=0.76) can be
recommended in Pilot S1 and S2 respectively.
Abstract: Because of architectural condition and structure application, sometimes mass source and stiffness source are not coincidence, and the structure is irregular. The structure is also might be asymmetric as an asymmetric bracing in plan which leads to unbalance distribution of stiffness or because of unbalance distribution of the mass. Both condition lead to eccentricity and torsion in the structure. The deficiency of ordinary code to evaluate the performance of steel structures against earthquake has been caused designing based on performance level or capacity spectrum be used. By using the mentioned methods it is possible to design a structure that its behavior against different earthquakes be predictive. In this article 5- story buildings with different percentage of asymmetric which is because of stiffness changes and kind of bracing (x and chevron bracing) have been designed. The static and dynamic nonlinear analysis under three acceleration recording has been done. Finally performance level of the structure has been evaluated.
Abstract: A procedural-animation-based approach which rapidly
synthesize the adaptive locomotion for quadruped characters that they
can walk or run in any directions on an uneven terrain within a
dynamic environment was proposed. We devise practical motion
models of the quadruped animals for adapting to a varied terrain in a
real-time manner. While synthesizing locomotion, we choose the
corresponding motion models by means of the footstep prediction of
the current state in the dynamic environment, adjust the key-frames of
the motion models relying on the terrain-s attributes, calculate the
collision-free legs- trajectories, and interpolate the key-frames
according to the legs- trajectories. Finally, we apply dynamic time
warping to each part of motion for seamlessly concatenating all desired
transition motions to complete the whole locomotion. We reduce the
time cost of producing the locomotion and takes virtual characters to
fit in with dynamic environments no matter when the environments are
changed by users.
Abstract: Artificial Neural Networks (ANNs) have been used successfully in many scientific, industrial and business domains as a method for extracting knowledge from vast amounts of data. However the use of ANN techniques in the sporting domain has been limited. In professional sport, data is stored on many aspects of teams, games, training and players. Sporting organisations have begun to realise that there is a wealth of untapped knowledge contained in the data and there is great interest in techniques to utilise this data. This study will use player data from the elite Australian Football League (AFL) competition to train and test ANNs with the aim to predict the onset of injuries. The results demonstrate that an accuracy of 82.9% was achieved by the ANNs’ predictions across all examples with 94.5% of all injuries correctly predicted. These initial findings suggest that ANNs may have the potential to assist sporting clubs in the prediction of injuries.
Abstract: This paper illustrates why existing technology
acceptance models are only of limited use for predicting and
explaining the adoption of future information and communication
technologies. It starts with a general overview over technology
adoption processes, and presents several theories for the acceptance
as well as adoption of traditional information technologies. This is
followed by an overview over the recent developments in the area of
information and communication technologies. Based on the
arguments elaborated in these sections, it is shown why the factors
used to predict adoption in existing systems, will not be sufficient for
explaining the adoption of future information and communication
technologies.
Abstract: Identifying protein coding regions in DNA sequences is a basic step in the location of genes. Several approaches based on signal processing tools have been applied to solve this problem, trying to achieve more accurate predictions. This paper presents a new predictor that improves the efficacy of three techniques that use the Fourier Transform to predict coding regions, and that could be computed using an algorithm that reduces the computation load. Some ideas about the combination of the predictor with other methods are discussed. ROC curves are used to demonstrate the efficacy of the proposed predictor, based on the computation of 25 DNA sequences from three different organisms.
Abstract: The survival of publicly listed companies largely
depends on their stocks being liquidly traded. This goal can be
achieved when new investors are attracted to invest on companies-
stocks. Among different groups of investors, individual investors are
generally less able to objectively evaluate companies- risks and
returns, and tend to be emotionally biased in their investing
decisions. Therefore their decisions may be formed as a result of
perceived risks and returns, and influenced by companies- images.
This study finds that perceived risk, perceived returns and trust
directly affect individual investors- trading decisions while attitude
towards brand partially mediates the relationships. This finding
suggests that, in courting individual investors, companies still need to
perform financially while building a good image can result in their
stocks being accepted quicker than the stocks of good performing
companies with hidden images.
Abstract: Cosmic showers, from their places of origin in space,
after entering earth generate secondary particles called Extensive Air
Shower (EAS). Detection and analysis of EAS and similar High
Energy Particle Showers involve a plethora of experimental setups
with certain constraints for which soft-computational tools like
Artificial Neural Network (ANN)s can be adopted. The optimality
of ANN classifiers can be enhanced further by the use of Multiple
Classifier System (MCS) and certain data - dimension reduction
techniques. This work describes the performance of certain data
dimension reduction techniques like Principal Component Analysis
(PCA), Independent Component Analysis (ICA) and Self Organizing
Map (SOM) approximators for application with an MCS formed
using Multi Layer Perceptron (MLP), Recurrent Neural Network
(RNN) and Probabilistic Neural Network (PNN). The data inputs are
obtained from an array of detectors placed in a circular arrangement
resembling a practical detector grid which have a higher dimension
and greater correlation among themselves. The PCA, ICA and SOM
blocks reduce the correlation and generate a form suitable for real
time practical applications for prediction of primary energy and
location of EAS from density values captured using detectors in a
circular grid.
Abstract: This paper presents a new adaptive DMC controller
that improves the controller performance in case of plant-model
mismatch. The new controller monitors the plant measured output,
compares it with the model output and calculates weights applied to
the controller move. Simulations show that the new controller can
help improve control performance and avoid instability in case of
severe model mismatches.
Abstract: This paper presents the prediction of kidney
dysfunction using different neural network (NN) approaches. Self
organization Maps (SOM), Probabilistic Neural Network (PNN) and
Multi Layer Perceptron Neural Network (MLPNN) trained with Back
Propagation Algorithm (BPA) are used in this study. Six hundred and
sixty three sets of analytical laboratory tests have been collected from
one of the private clinical laboratories in Baghdad. For each subject,
Serum urea and Serum creatinin levels have been analyzed and tested
by using clinical laboratory measurements. The collected urea and
cretinine levels are then used as inputs to the three NN models in
which the training process is done by different neural approaches.
SOM which is a class of unsupervised network whereas PNN and
BPNN are considered as class of supervised networks. These
networks are used as a classifier to predict whether kidney is normal
or it will have a dysfunction. The accuracy of prediction, sensitivity
and specificity were found for each type of the proposed networks
.We conclude that PNN gives faster and more accurate prediction of
kidney dysfunction and it works as promising tool for predicting of
routine kidney dysfunction from the clinical laboratory data.
Abstract: In this paper, we study the application of Extreme
Learning Machine (ELM) algorithm for single layered feedforward
neural networks to non-linear chaotic time series problems. In this
algorithm the input weights and the hidden layer bias are randomly
chosen. The ELM formulation leads to solving a system of linear
equations in terms of the unknown weights connecting the hidden
layer to the output layer. The solution of this general system of
linear equations will be obtained using Moore-Penrose generalized
pseudo inverse. For the study of the application of the method we
consider the time series generated by the Mackey Glass delay
differential equation with different time delays, Santa Fe A and
UCR heart beat rate ECG time series. For the choice of sigmoid,
sin and hardlim activation functions the optimal values for the
memory order and the number of hidden neurons which give the
best prediction performance in terms of root mean square error are
determined. It is observed that the results obtained are in close
agreement with the exact solution of the problems considered
which clearly shows that ELM is a very promising alternative
method for time series prediction.
Abstract: The physical methods for RNA secondary structure prediction are time consuming and expensive, thus methods for computational prediction will be a proper alternative. Various algorithms have been used for RNA structure prediction including dynamic programming and metaheuristic algorithms. Musician's behaviorinspired harmony search is a recently developed metaheuristic algorithm which has been successful in a wide variety of complex optimization problems. This paper proposes a harmony search algorithm (HSRNAFold) to find RNA secondary structure with minimum free energy and similar to the native structure. HSRNAFold is compared with dynamic programming benchmark mfold and metaheuristic algorithms (RnaPredict, SetPSO and HelixPSO). The results showed that HSRNAFold is comparable to mfold and better than metaheuristics in finding the minimum free energies and the number of correct base pairs.
Abstract: Two-phase frictional pressure drop data were
obtained for condensation of carbon dioxide in single horizontal
micro tube of inner diameter ranged from 0.6 mm up to 1.6 mm over
mass flow rates from 2.5*10-5 to 17*10-5 kg/s and vapor qualities
from 0.0 to 1.0. The inlet condensing pressure is changed from 33.5
to 45 bars. The saturation temperature ranged from -1.5 oC up to 10
oC. These data have then been compared against three (two-phase)
frictional pressure drop prediction methods. The first method is by
Muller-Steinhagen and Heck (Muller-Steinhagen H, Heck K. A
simple friction pressure drop correlation for two-phase flow in pipes.
Chem. Eng. Process 1986;20:297–308) and that by Gronnerud R.
Investigation of liquid hold-up, flow-resistance and heat transfer in
circulation type evaporators, part IV: two-phase flow resistance in
boiling refrigerants, Annexe 1972. Then the method used by
FriedelL. Improved friction pressures drop in horizontal and vertical
two-phase pipe flow. European Two-Phase Flow Group Meeting,
Paper E2; 1979 June, Ispra, Italy. The methods are used by M.B Ould
Didi et al (2001) “Prediction of two-phase pressure gradients of
refrigerant in horizontal tubes". Int.J.of Refrigeration 25(2002) 935-
947. The best available method for annular flow was that of Muller-
Steinhagen and Heck. It was observed that the peak in the two-phase
frictional pressure gradient is at high vapor qualities.